08-effective-communication

Professor Shannon Ellis

2/7/23

Effective Communication

Q&A

Q: for the last part of lecture 07, I tried glance(m_ht_wt) but it didn’t work because m_ht_wt doesn’t exist. Is “m_ht_wt” supposed be a model?
A: Yup, this model was the height by weight model:

m_ht_wt <- linear_reg() |>
  set_engine("lm") |>
  fit(Height_in ~ Width_in, data = pp)

Q: I was not too sure what was going on when talking about the relationship between painting height and school.
A: I don’t think you were the only one confused! Briefly here (and I’m happy to chat more before/after class and in OH), we were looking to determine/quantify the relationship between the size (height) of a painting and the school from which the painting originated. This was an example of having more than two categories for a categorical (factor) predictor. The important points were undersatnding that each level is compared to the baseline and the linear model that results from multiple categories. Part 4 of the lab gets into this a bit more too. Definitely follow up if you’re unsure after doing that part of the lab!

Q: How do you calculate the linear regression model when you have non-numeric values? For example, on lab 04, when it asks to calculate the linear regression model by gender, the gender appears only as male and female. Suppose male is 1 and female is 0 (interpreted by the function), then male linear regression model is y =ax + 1?
A: Close! the “1” would be plugged in as the value of x (in what you suggested)m not for the intercept. So the function would be \(y=\beta_1*1 + \beta_0\)

Course Announcements

Lab04 due Friday
HW02 due Monday

Practice Midterms Now Available
- answers posted next week
Midterm Exam
- will cover material through “Multiple Linear Regression”
- will be released/posted next Friday after lab
- will be due Monday Nov 6th at 11:59 PM
- will be an Rmd document and submitted via GitHub (like everything so far)
- will be completed individually (open Notes; open Internet)

Link for Later

Agenda

Communicating for your audience
Oral Communication
Written Communication
Visual Communication

Consider your audience

What does this mean?

❓ What does it mean to “consider your audience?”

Simply: You do the work so they don’t have to.

…also the aesthetic-usability effect exists.

What’s the right level?

General Audience
✔ background
🚫 limit technical details
🎉 emphasize take-home

Technical Audience
⬇ limit background
💻 all-the-details
🎉 emphasize take-home

Considerations

Platform: written? oral?

Setting: informal? formal?

Timing: never go over your time limit!

Storytelling

Stories have a beginning, a middle, and an end.

Stories do not need every detail of what you’ve tried

Reports and presentations should tell a story
Planning out your report/presentation can help

Hold the audience’s attention with what needs to be said; do so effectively
Tell your audience why they should care; why it matters
You should explain your choices and the “why”

Choose informative titles

On presentations: Balance b/w short and informative (goal: concise)

Avoid: “Analyzing NHANES”

Better: “Data from the NHANES study shows that diet is related to overall health”

On visualizations: emphasize the take-home! (what’s learned or what action to take)

Avoid: “Boxplot of gender”

Better: “Twice as many females as males included for analysis”

Avoid: “Tickets vs. Time”

Better: “Staff unable to respond to incoming tickets; need to hire 2 FTEs”

Effective Oral Communication

Brainstorm: Advice You’ve Been Given?

Student responses

Student responses will be added to notes after class…

Presentations are for listening

Advantage: words to explain out loud what you’re showing

You are presenting for the person in the back of the room.

To accomplish:

don’t read directly off slides
repetition is ok: tell what you’re going to tell them, tell them, tell them what you told them
use animation to build your story (not to distract)
introduce your axes
text/labels larger
watch your speech speed
practice!

For Example: A Happy Ending for (almost) everyone in Little Red Riding Hood

Red Riding Hood (RRH) has to walk 0.54 mi from Point A (home) to Point B (Grandma’s)
RRH meets Wolf who (1) runs ahead to Grandma’s, (2) eats her, and (3) dresses in her clothes
RRH arrives at Grandmas at 2PM, asks her three questions
Identified problem: after third question, Wolf eats RRH
Solution: vendor (Woodsman) employs tool (ax)
Expected outcome: Grandma and RRH alive, wolf is not

Little Red Riding Hood

Effective Written Communication

Brainstorm: Advice You’ve Been Given?

Student responses

Student responses will be added to notes after class…

Benefits of written communciation

Your audience has time to process…but the explanation has to be there!

Visually: more on a single visualization

Yes, often there are different visualizations for reports/papers than for presentations/lectures.

When you have time to digest (read)

❓ What makes this an effective visualization for a written communication?”

Source: Storytelling wtih data by cole nussbaumer knaflic

Written Explanations

Visualizations should be explained/interpreted
Models should be explained
- should be clear what question is being answered
- what conclusions is being drawn
- and what numbers were used to draw that conclusion

Data Science Reports in .Rmd

As concise as possible
Necessary details (for your audience); nothing more
- Be sure that the knit output contains what you intended (plots displayed; headers etc.)
- …and does NOT display stuff that doesn’t need to be there (messages/warnings suppressed, brainstorming, etc.)
Typical Sections: Introduction/Background, Setup, Data, Analysis, Conclusion, References

Controlling HTML document settings

Table of Contents

---
title: "Document Title"
output:
  html_document:
    toc: true
    toc_float: true
---

Theme

---
title: "Document Title"
output:
  html_document:
    theme: united
    highlight: tango
---

Figure Options

---
title: "Document Title"
output:
  html_document:
    fig_width: 7
    fig_height: 6
    fig_caption: true
---

Code Folding

---
title: "Document Title"
output:
  html_document:
    code_folding: hide
---

Controlling code chunk output

Specified in the curly braces, separated by commas

eval: whether to execute the code chunk
echo: whether to include the code in the output
warning, message, and error: whether to show warnings, messages, or errors in the knit document
fig.width and fig.height: control the width/height of plots

Controlling for the whole document:

knitr::opts_chunk$set(fig.width = 8, collapse = TRUE)

Editing & Proofreading

Did you end up telling a story?
- Things missing?
- Things to delete?

Do not fall in love with your words/code/plots

Do spell check
Do read it over before sending/presenting/submitting

Aside: Citing Sources

When are citations needed?

“We will be doing our analysis using two different data sets created by two different groups: Donohue and Mustard + Lott, or simply Lott”

“What turned from the idea of carrying firearms to protect oneself from enemies such as the British monarchy and the unknown frontier of North America has now become a nationwide issue.”

“Right to Carry Laws refer to laws that specify how citizens are allowed to carry concealed handguns when they’re away from home without a permit”

“In this case study, we are examining the relationship between unemployment rate, poverty rate, police staffing, and violent crime rate.”

“In the United States, the second amendment permits the right to bear arms, and this law has not been changed since its creation in 1791.”

“The Right to Carry Laws (RTC) is defined as”a law that specifies if and how citizens are allowed to have a firearm on their person or nearby in public.””

Reminder: You do NOT get docked points for citing others’ work. You can be at risk of AI Violation if you don’t. When in doubt, give credit.

Footnotes in .Rmd

How to specify a footnote in text:

Here is some body text.[^1]

How to include the footnote’s reference:

[^1]: This footnote will appear at the bottom of the page.

Effective Visual Communication

Brainstorm: Advice You’ve Been Given?

Student Responses

Student responses will be added to notes after class…

The Glamour of Graphics

builds on top of the grammar (components) of a graphic
considerations for the design of a graphic
color, typography, layout
going from accurate to 😍effective

Left-align titles at top-left

😬 Accurate

ggplot(penguins, aes(x = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

😍 Effective

ggplot(penguins, aes(x = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
        plot.title.position = "plot")

Avoid head-tilting

😬 Accurate

ggplot(penguins, aes(x = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
        plot.title.position = "plot")

😍 Effective

ggplot(penguins, aes(y = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme(plot.title.position = "plot")

Borders & Backgrounds: 👎

😬 Accurate

ggplot(penguins, aes(y = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_bw() +
  theme(plot.title.position = "plot")

😍 Effective

ggplot(penguins, aes(y = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_minimal() +
  theme(plot.title.position = "plot")

Organize & Remove/Lighten as much as possible

😬 Accurate

ggplot(penguins, aes(y = species, fill = species)) +
  geom_bar() +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_minimal() +
  theme(plot.title.position = "plot")

😍 Effective

ggplot(penguins, aes(y = fct_rev(fct_infreq(species)), fill = species)) +
  geom_bar() +
  geom_text(stat='count', aes(label=..count..), hjust = 1.5, color = "white", size = 6) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_fill_manual(values = c("#454545", rep("#adadad", 2))) +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_minimal(base_size = 18) +
  theme(axis.text.x = element_blank(),
        plot.title.position = "plot", 
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        axis.title = element_blank())

Legends suck

😬 Accurate

ggplot(penguins, aes(y = fct_rev(fct_infreq(species)), fill = species)) +
  geom_bar() +
  geom_text(stat='count', aes(label=..count..), hjust = 1.5, color = "white", size = 6) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_fill_manual(values = c("#454545", rep("#adadad", 2))) +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_minimal(base_size = 18) +
  theme(axis.text.x = element_blank(),
        plot.title.position = "plot", 
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        axis.title = element_blank())

😍 Effective

ggplot(penguins, aes(y = fct_rev(fct_infreq(species)), fill = species)) +
  geom_bar() +
  geom_text(stat='count', aes(label=..count..), hjust = 1.5, color = "white", size = 7) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_fill_manual(values = c("#454545", rep("#adadad", 2))) +
  labs(title = "Adelie Penguins are the most common in Antarctica", 
       subtitle = "Frequency of each penguin species studied near Palmer Station, Antarctica") +
  theme_minimal(base_size = 20) +
  theme(axis.text.x = element_blank(),
        plot.title.position = "plot", 
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        axis.title = element_blank(),
        legend.position = "none")

Additional Guidance

White space is like garlic - take the amount you need and triple it
Fonts Matter
Use Color Effectively

08-effective-communication

Effective Communication

Q&A

Course Announcements

Agenda

Suggested Reading

Consider your audience

What does this mean?

What’s the right level?

Considerations

Storytelling

Choose informative titles

Effective Oral Communication

Student responses

Presentations are for listening

For Example: A Happy Ending for (almost) everyone in Little Red Riding Hood

Little Red Riding Hood

Effective Written Communication

Student responses

Benefits of written communciation

When you have time to digest (read)

Written Explanations

Data Science Reports in .Rmd

Controlling HTML document settings

Controlling code chunk output

Editing & Proofreading

Aside: Citing Sources

Footnotes in .Rmd

Effective Visual Communication

Student Responses

The Glamour of Graphics

Left-align titles at top-left

Avoid head-tilting

Borders & Backgrounds: 👎

Organize & Remove/Lighten as much as possible

Legends suck

Additional Guidance